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Advanced Sensor Fusion for Railway Security - A Hierarchical Graph-Based Approach

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Abstract

Ensuring effective security surveillance in the railway sector presents a number of significant challenges, largely due to the extensive and dispersed nature of railway infrastructure. To effectively address threats such as vandalism, trespassing and sabotage, it is essential to implement continuous and comprehensive monitoring systems. Despite recent advances in technology, the issue of false alarms and information overload remains a significant challenge, undermining the efficiency of security operations. To overcome these challenges, recent studies have investigated the use of multi-modal fusion systems, which have proven effective in reducing false alarms. This paper introduces a Hierarchical Fusion Graph (HFG) as a generalised approach for interpreting a fusion system as a directed acyclic graph (DAG), enhancing the scalability and flexibility of sensor data fusion.
OriginalspracheEnglisch
Titel2024 Sensor Data Fusion: Trends, Solutions, Applications, SDF 2024
Seiten1-7
Seitenumfang7
ISBN (elektronisch)979-8-3315-2744-0
DOIs
PublikationsstatusVeröffentlicht - 5 Feb. 2025
Veranstaltung2024 Sensor Data Fusion: Trends, Solutions, Applications (SDF) - Bonn, Bonn, Deutschland
Dauer: 25 Nov. 202427 Nov. 2024

Publikationsreihe

Name2024 Sensor Data Fusion: Trends, Solutions, Applications, SDF 2024

Konferenz

Konferenz2024 Sensor Data Fusion: Trends, Solutions, Applications (SDF)
Land/GebietDeutschland
StadtBonn
Zeitraum25/11/2427/11/24

Research Field

  • Responsive Sensing & Analytics

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